scholarly journals An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 1975-1985 ◽  
Author(s):  
Wei You ◽  
Changqing Shen ◽  
Dong Wang ◽  
Liang Chen ◽  
Xingxing Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Learning Method ◽  
Activation Functions ◽  
Machine Fault Diagnosis ◽  
Deep Feature ◽  
Machine Fault ◽  
Deep Feature Learning

scholarly journals A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4522
Author(s):  
Xihui Chen ◽  
Aimin Ji ◽  
Gang Cheng
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Feature Learning ◽  
Planetary Gear ◽  
Learning Ability ◽  
Energy Industry ◽  
Deep Feature ◽  
Gear Fault ◽  
Deep Feature Learning ◽  
Electromechanical Equipment

Planetary gear is the key component of the transmission system of electromechanical equipment for energy industry, and it is easy to damage, which affects the reliability and operation efficiency of electromechanical equipment of energy industry. Therefore, it is of great significance to extract the useful fault features and diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed. First, to improve the data learning ability and the robustness of feature extraction process of AE model, the sparse autoencoder (SAE) and the contractive autoencoder (CAE) were studied, respectively. Then, the quantum ant colony algorithm (QACA) was used to optimize the specific location and key parameters of SAEs and CAEs in deep learning architecture, and multiple SAEs and multiple CAEs were stacked alternately to form a novel deep learning architecture, which gave the deep learning architecture better data learning ability and robustness of feature extraction. The experimental results show that the proposed method can address the raw vibration signals of planetary gear. Compared with other deep learning architectures and shallow learning architecture, the proposed method has better diagnosis performance, and it is an effective method of deep feature learning and fault diagnosis.

Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

2018 ◽  
Vol 305 ◽  
pp. 1-14 ◽  
Author(s):  
Shenghao Tang ◽  
Changqing Shen ◽  
Dong Wang ◽  
Shuang Li ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Machinery Fault Diagnosis
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